{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "84f946ab-c6c2-45f8-9785-245ab54a3cef",
   "metadata": {},
   "source": [
    " # 计算机中数的表示\n",
    "\n",
    "\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e81901f6-8abd-48e6-a490-dc7a11099ed0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17179869184\n"
     ]
    }
   ],
   "source": [
    "print(2**30*16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "38c60b34-4bf2-437e-af2c-3cd5963fc9cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54.83689498901367\n"
     ]
    }
   ],
   "source": [
    "print(4378*4378*3/1024/1024)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e5d714d9-0706-44e9-bf40-84c222d24613",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "298.77694576402365\n"
     ]
    }
   ],
   "source": [
    "print(2**30*16/4378/4378/3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "10d77225-b7b1-4d04-937d-6c907591552c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "288.0\n"
     ]
    }
   ],
   "source": [
    "print(2**20*12**2*2/1024/1024)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1b898f58-2e7a-487f-9113-8318ee3d018c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9062a38a-cc1b-4348-b13c-81393466365c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting velocity_earth.csv\n"
     ]
    }
   ],
   "source": [
    "%%writefile velocity_earth.csv\n",
    "name,velocity,earth\n",
    "苹果,0.1,falldown\n",
    "网球,50,falldown\n",
    "羽毛球,63,falldown\n",
    "大炮,600,falldown\n",
    "子弹,850,falldown\n",
    "鬼怪,2370,falldown\n",
    "防空导弹,3000,falldown\n",
    "神7,7820,flyout\n",
    "新视野号,16260.0,flyout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8c73a4ba-7bae-4b6b-a293-3194e4039eda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>velocity</th>\n",
       "      <th>earth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>苹果</td>\n",
       "      <td>0.1</td>\n",
       "      <td>falldown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>网球</td>\n",
       "      <td>50.0</td>\n",
       "      <td>falldown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>羽毛球</td>\n",
       "      <td>63.0</td>\n",
       "      <td>falldown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>大炮</td>\n",
       "      <td>600.0</td>\n",
       "      <td>falldown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>子弹</td>\n",
       "      <td>850.0</td>\n",
       "      <td>falldown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>鬼怪</td>\n",
       "      <td>2370.0</td>\n",
       "      <td>falldown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>防空导弹</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>falldown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>神7</td>\n",
       "      <td>7820.0</td>\n",
       "      <td>flyout</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>新视野号</td>\n",
       "      <td>16260.0</td>\n",
       "      <td>flyout</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   name  velocity     earth\n",
       "0    苹果       0.1  falldown\n",
       "1    网球      50.0  falldown\n",
       "2   羽毛球      63.0  falldown\n",
       "3    大炮     600.0  falldown\n",
       "4    子弹     850.0  falldown\n",
       "5    鬼怪    2370.0  falldown\n",
       "6  防空导弹    3000.0  falldown\n",
       "7    神7    7820.0    flyout\n",
       "8  新视野号   16260.0    flyout"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"velocity_earth.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "de0b258f-bd11-4377-971e-eb204fe569ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4      子弹\n",
       "5      鬼怪\n",
       "6    防空导弹\n",
       "7      神7\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['name'][4:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7018d960-0415-4861-9bf4-ae13991af9d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing Year_Money.csv\n"
     ]
    }
   ],
   "source": [
    "%%writefile Year_Money.csv\n",
    "Year,Money\n",
    "1990,38\n",
    "1991,49\n",
    "1992,53\n",
    "1993,58\n",
    "1994,65\n",
    "1995,70"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2300c660-7800-4595-b76f-2ed504112ff3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Money</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1990</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1991</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1992</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1993</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1994</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1995</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Money\n",
       "0  1990     38\n",
       "1  1991     49\n",
       "2  1992     53\n",
       "3  1993     58\n",
       "4  1994     65\n",
       "5  1995     70"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ym=pd.read_csv('Year_Money.csv')\n",
    "ym"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3fa78ca3-07f9-46f7-aef5-1a3f0d73e401",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(ym['Year'],ym['Money'])\n",
    "ax.scatter(ym['Year'],ym['Money'])\n",
    "ax.set(xlabel='Time(year)', ylabel='GDP(Trillion dollars)',\n",
    "       title='GDP in 1990-1995')\n",
    "ax.grid()\n",
    "\n",
    "fig.savefig(\"test.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3a62f32f-6f77-49ad-9f6f-a250dd118158",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25.766174801362087"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "227.0/8.81"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6c0569f2-5fa6-4363-894d-7f0ba07b3e3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c316d010-7cb6-41b9-a942-cdd68cb9d313",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array(range(64))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f21a7778-0da9-4d88-a7fc-a4307d715f9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
       "       51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "de52b970-98cd-4fe2-9d4d-1bb420387860",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "238480e6-d84f-42ec-9288-e74fc01c03bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3,  4,  5,  6,  7],\n",
       "        [ 8,  9, 10, 11, 12, 13, 14, 15],\n",
       "        [16, 17, 18, 19, 20, 21, 22, 23],\n",
       "        [24, 25, 26, 27, 28, 29, 30, 31]],\n",
       "\n",
       "       [[32, 33, 34, 35, 36, 37, 38, 39],\n",
       "        [40, 41, 42, 43, 44, 45, 46, 47],\n",
       "        [48, 49, 50, 51, 52, 53, 54, 55],\n",
       "        [56, 57, 58, 59, 60, 61, 62, 63]]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape = (2,4,8)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "436eedfe-298b-4cf2-a6ce-a1e48a227c77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "t=np.arange(361)\n",
    "rad=t*np.pi/180\n",
    "s=np.sin(rad)\n",
    "c=np.cos(rad)\n",
    "\n",
    "plt.plot(t, s, color='red', linewidth=2, label=\"sin\")\n",
    "plt.plot(t, c, color='blue', linewidth=2, label=\"cos\")\n",
    "plt.title('sin and cos') \n",
    "plt.xlabel('rad') \n",
    "plt.ylabel(\"value\") \n",
    "plt.grid(True, alpha=0.6)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8bfbbd85-fedc-4e90-afa3-2f15b22ee401",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:base] *",
   "language": "python",
   "name": "conda-base-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
